The　majority　of　existing　studies　in　subway　train　operations　focus　on　timetable　optimization　and　vehicle　tracking　methods,　which　may　be　infeasible　with　disturbances　in　actual　operations.　To　deal　with　uncertain　passenger　demands　and　realize　real-time　train　operations　(RTOs)　satisfying　multiobjectives,　including　overspeed　protection,　punctuality,　riding　comfort,　and　energy　consumption,　this　paper　proposes　two　RTO　algorithms　via　expert　knowledge　and　an　online　learning　approach.　The　first　RTO　algorithm　is　developed　by　a　knowledge-based　system　to　ensure　the　multiple　objectives　with　a　constant　timetable.　Then,　by　considering　uncertain　passenger　demand　at　each　station　and　random　running　time　errors,　we　convert　the　train　operation　problem　into　a　Markov　decision　process　with　nondeterministic　state　transition　probabilities　in　which　the　aim　is　to　minimize　the　reward　for　both　the　total　time　delay　and　energy　consumption　in　a　subway　line.　After　designing　policy,　reward,　and　transition　probability,　we　develop　an　integrated　train　operation　(ITO)　algorithm　based　on　Q-learning　to　realize　RTOs　with　online　adjusting　the　timetable.　Finally,　we　present　some　numerical　examples　to　test　the　proposed　algorithms　with　real　detected　data　in　the　Yizhuang　Line　of　Beijing　Subway.　The　results　indicate　that,　taking　the　multiple　objectives　into　account,　the　RTO　algorithm　outperforms　both　manual　driving　and　automatic　train　operations.　In　addition,　the　ITO　algorithm　is　capable　of　dealing　with　uncertain　disturbances,　keeping　the　total　time　delay　within　2　s　and　reducing　the　energy　consumption.